Tianyu Xiong's Personal Website

In the era of information overload, the value of recommender systems has been profoundly recognized in academia and industry alike. Multi-interest sequential recommendation, in particular, is a subfield that has been receiving increasing attention in recent years. By generating multiple user representations, multi-interest learning models demonstrate superior expressiveness than single user representation models, both theoretically and empirically. Despite major advancements in the field, three major issues continue to plague the performance and adoptability of multi-interest learning methods, the difference between training and deployment objectives, the inability to access item information, and the difficulty of industrial adoption due to its single-tower architecture. We address these challenges by proposing a novel multi-tower multi-interest framework with user representation repel. Experimental results across multiple large-scale industrial datasets proved the effectiveness and generalizability of our proposed framework.

Although considerable success has been achieved in urban air quality prediction (AQP) with machine learning techniques, accurate and long-term prediction is still challenging. One of the key issues for existing AQP approaches is that air quality monitoring stations are sparsely distributed, typically with around ten monitoring stations per city. As air quality may change abruptly in a local area, it is difficult to perform AQP accurately in areas that are far away from observation points. In addition, due to the large distance between every two monitoring stations, we cannot effectively leverage spatial relations among them to improve the AQP accuracy. In this paper, thanks to the development of low-cost air quality sensors, we are now able to collect a large-scale air quality dataset with 393 deployed air quality monitoring stations in a 120 km × 70 km region, which is more than ten times denser than existing AQP datasets. Further, we present a novel method to handle the data effectively. Specifically, we first convert the observed data from irregularly distributed monitoring stations into a regular image-like pollution map, which can then be processed with advanced deep convolutional networks. The experimental results show that the proposed approach can simultaneously model the temporal and spatial relations in our large-scale densely-observed dataset, leading to significantly improved AQP results

The ultimatum game (UG) is a useful game model for investigating the evolution of fairness. In this paper, considering the similarity between individuals, we introduce a similarity parameter into the spatial UG and focus on the evolution of the average offer and acceptance threshold. Under this mechanism, individuals can be either more generous or stingier to those who they are similar to. The simulation result shows that the fairness of the system decreases when the strategy is affected by the similarity between players. The greater the influence of similarity, the more fairness is decreased. Equal treatment, hence, is the best way to obtain fairness. Our results may provide some critical insights into the effect of similarity and favoritism on fairness among people.